Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods

Abstract

Droughts can cause significant damage to agricultural and other systems. An important aspect of mitigating the impacts of drought is an effective method of forecasting future drought events. In this study, five methods of forecasting drought for short lead times were explored in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) was the drought index chosen to represent drought in the basin. Machine learning techniques including artificial neural networks (ANNs) and support vector regression (SVR) were compared with coupled models (WA-ANN and WA-SVR) which pre-process input data using wavelet analysis (WA). This study proposed and tested the SVR and WA-SVR methods for short term drought forecasting. This study also used only the approximation series (derived via wavelet analysis) as inputs to the ANN and SVR models, and found that using just the approximation series as inputs for models gave good forecast results. The forecast results of all five data driven models were compared using several performance measures (RMSE, MAE, R2 and a measure of persistence). The forecast results of this study indicate that the coupled wavelet neural network (WA-ANN) models were the best models for forecasting SPI 3 and SPI 6 values over lead times of 1 and 3 months in the Awash River Basin.